The tempting shortcut
A language model can turn a pile of records into polished prose. In a regulated workflow, polish is not the difficult part. The difficult part is knowing whether every identifier, date, event type, and reason in that prose came from a record the system actually retrieved.
SignalWatch is being built around that distinction. The model is not treated as the factual source. Public FDA records are.
Four boundaries, not one prompt
The current architecture separates the work into explicit stages. That makes failures easier to locate and gives the final reviewer a source trail instead of a confidence score.
- Collect and store source records with their identifiers and source links.
- Match stored records against client-specific watch terms.
- Let the model select and order only facts that exist in the prepared fact set.
- Validate the selection, then render the final brief in application code.
What this does not solve
Source-constrained generation does not make the product regulatory advice, guarantee that a search captured every relevant event, or remove the need for professional review. It narrows one failure mode: invented factual detail in a generated brief.
The scheduled production monitor is not running today. The pipeline has been demonstrated using historical data; production freshness is still an operational task, not a marketing claim.